Foreword

The past decade produced a rich ecosystem of web sites that provide personalized access to (semi-)structured data: financial asset tracking and management sites, personalized news delivery services, and even customized web search engines are all but a few examples. More recently a second wave of innovation has been fueled by the explosive growth of web platforms that enable rich online social interactions, such as online social networks, web communities, wikis, mashups, and folksonomies. These new applications go beyond personalized information access and dissemination. Users can now transcend their role of passive content consumers and engage in content creation, sharing, and various forms of online collaboration as well.

Both the more traditional and the newer applications rely critically on user-specific pieces of information-such as profile data, preferences, activity logs, location, group memberships, and social connections-to provide a personalized experience. In this context, there is an ongoing need for novel user-centric, context-aware database systems that provide specific storage and processing of user and context data. Additionally, many new social applications present challenges of unprecedented scale and complexity, and room for innovative mining of the socially-affected user data.

Arguably, the above challenges have led to a true renaissance of database research, affecting many areas from core database architecture and algorithms to data mining. The jury is still out there on which problems require genuinely novel solutions instead of simply wiping off the dust on some older journals. Meanwhile, we are witnessing a lot of energy and excitement around research efforts that try to address problems arising around personalized, contextual, and social data.

Such data is one of the motivators of modern architecture research. At the lowest level, the sheer volume of the data and its highly distributed nature, often coupled with relaxed consistency requirements, leads to a plethora of basic architectural solutions that depart to various extents from classical relational paradigms. The research community is working hard to bring the best of its findings to industrial applications.

The interconnected nature of personalized, social, and contextual data management problems, and the fertile research ground they represent motivates a corresponding discussion within the database community. Teams studying and building different components of database systems need to clarify their views of personalization and contextualization, and expose their approaches to the rest of the community. We need a common understanding of new challenges and collaborate on the design of new models, algorithms, and systems for emerging applications. The PersDB 2011 workshop aims at providing the appropriate venue for discussion and debate of personalization and contextualization issues and at nurturing related future research and applications.